Prediction method of wheat moisture content in the hot air drying process based on backpropagation neural network optimized by genetic algorithms

Abstract

It is necessary to grasp the moisture content of wheat in the process of hot air drying in real time to save energy and improve quality. According to the result of the hot air drying experiment, a backpropagation (BP) neural network prediction model with a topology of 2-4-1 is established. The mean absolute error (MAE) and degree of fit (R2) of the predicted results are 0.216% and 0.9804. The BP neural network model is optimized with a genetic algorithm (GA-BP) to improve accuracy. The MAE and R2 are down to 0.069% and 0.9945. The generalization ability of the GA-BP prediction model is verified, when predicting the samples out of the training set, the MAE is 0.37% and R2 is 0.9993. It shows that the GA-BP prediction model is of good generalization ability. This study provides a new conception for the real-time control of moisture content in hot air drying wheat. Novelty impact statement The prediction model of wheat moisture content in the hot air drying process was established based on BP neural network algorithm and optimized by the genetic algorithm. The wheat moisture content can be accurately predicted at different hot air temperatures and drying times, which provides a new method for the real-time monitoring of moisture content in the drying process. According to the predicted results, the changes in the moisture content of wheat are grasped in advance, which solve the problem that the moisture content of wheat is difficult to obtain in real time during the hot air drying process and hard to adjust the hot air temperature in time.